Face Recognition Age Determination via Derived Data Distribution
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Solution Overview
Problem
Current methods for determining the age of individuals in images rely on rough estimates based on appearance, leading to inaccurate age determination due to factors like lighting conditions and image quality.
Innovation Solution
A method for face recognition that generates a derived face data set with varying ages, performs age determination on each data point, and matches the age distribution interval to a reference interval to accurately determine the age of the face data, using a combination of deep learning and convolutional neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple different derived face data are generated and age determination is performed on each, then measurement precision of age recognition is improved, but device complexity increases
Solution Approach 1:
The method segments the age recognition task by generating multiple derived face data sets from the original face image, each representing different age possibilities. Age determination is then performed separately on each derived data set, allowing the system to aggregate results for more accurate age estimation. This segmentation transforms a single complex recognition task into multiple simpler sub-tasks that can be processed independently and combined.
Solution Approach 2:
The patent introduces a new dimension of analysis by creating derived face data that represents different age states. Instead of analyzing the original face image in a single dimension, the system generates multiple dimensional variations (younger and older versions) and performs age determination across these dimensions. The final age estimate is derived from the distribution pattern across all derived data sets, effectively adding an age-variation dimension to the analysis.
2Reliability
If face data samples are expanded through derived data generation, then reliability of age determination is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by generating derived face data sets before the final age determination. These derived data sets are pre-processed to represent different age states, and age determination is performed on each in advance. The results from these preliminary determinations are then aggregated to produce the final age estimate, allowing the system to account for various age possibilities before committing to a final result.
Solution Approach 2:
The method incorporates feedback mechanisms where the age determination results from multiple derived face data sets are fed back into the system for aggregation and analysis. The distribution pattern of ages across all derived data sets provides feedback that helps refine the final age estimate. This feedback loop allows the system to adjust and improve its age determination based on the collective information from all derived data sets.
Data Source
AI summary
A method and device for face recognition, a storage medium, and an electronic device are provided. The method includes the following. Face data to-be-tested is obtained. A first derived face data set related to the face data to-be-tested is generated according to the face data to-be-tested. For each of multiple derived face data in the first derived face data set, perform age determination, and generate an age distribution interval corresponding to the first derived face data set. Whether the age distribution interval matches a first reference age interval is determined. Upon determining that the age distribution interval matches the first reference age interval, age data corresponding to the face data to-be-tested is obtained according to the age distribution interval.